William Wang

Education

PhD, Carnegie Mellon University

Bio

William Wang is an assistant professor in the Department of Computer Science at the University of California, Santa Barbara. He received his PhD from School of Computer Science, Carnegie Mellon University. He has broad interests in machine learning approaches to data science, including statistical relational learning, information extraction, computational social science, speech, and vision. He has published more than 30 papers at leading conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, and EMNLP 2015, a best reviewer award at NAACL 2015, and the Richard King Mellon Presidential Fellowship in 2011. He is an alumnus of Columbia University, and a former research scientist intern of Yahoo! Labs, Microsoft Research Redmond, and University of Southern California. In addition to research, William enjoys writing scientific articles that impact the broader online community: his microblog has more than 2,000,000 views each month.

Research

I study the theoretical foundation and practical algorithms for Artificial Intelligence. To build intelligent machines that can tackle challenging reasoning problems under uncertainty, I have pursued answers via studies of Machine Learning, Natural Language Processing, and Interdisciplinary Data Science. More specifically, I am interested in designing scalable inference and learning algorithms to analyze massive datasets with complex structures. In particular, I advance methods in the following research areas: Statistical Relational Learning, Knowledge Representation and Reasoning, Natural Language Processing, Speech, and Computational Social Science. The central focus of my PhD dissertation research is to bring together all areas above and design scalable algorithms for large scale inference problems on knowledge graphs. Meanwhile, I enjoy collaborating with scientists and domain experts of different backgrounds for interdisciplinary research in data science. Currently, I am interested in advancing challenging problems in Artificial Intelligence, such as Natural Language Understanding, Information Extraction, and Learning to Reason.